D3.4 Report on Dynamic Data Assimilation methodology and site- Specific risk parameters and stressor indicators

This deliverable, titled “D3.4 - Report on Dynamic Data Assimilation methodology and site-specific risk parameters and stressor indicators”, aims to outline a new methodology developed by AUTh for the continuous update and refinement of numerical modelling results based on in-situ measurements. This approach relies on a 3D data assimilation protocol, enabling the integration of data streams from meteorological and hydrological stations in the selected use case areas. Specifically, the data collected from the sensor networks, delivered through the monitoring system, is initially compared with simulated data and subsequently analysed and integrated (including aggregation, synchronization, calibration, and assimilation) via a fully interoperable data management platform. This method explicitly introduces local-scale atmospheric and climate stresses on the Inland WaterWays (IWW) infrastructures and the connected land infrastructures of selected use cases, which are otherwise unresolved, into high-resolution simulations.
Furthermore, this deliverable aims also to provide a detailed description of the qualitative and quantitative assessment of the primary and secondary impact indicators derived from climate calculations and real-time (RT) in-situ measurements. These indicators will subsequently be used in WP5-WP7 of PLOTO as inputs for generating the climatic risk assessment corresponding to the selected transport infrastructures and the expected damages associated. The data used in the analysis described in this document originates from the EURO-CORDEX project, leveraging multiple Regional Climate Model (RCM) simulations performed by various Global Climate Models (GCMs) to cover the period from 1971 to 2100. The climatic variables considered in this analysis include ambient temperature and precipitation.
A key component of this deliverable is the validation of the newly implemented data assimilation framework within the Operational Modelling System (OMS). By integrating real-time weather station data into the model, this methodology enhances forecast accuracy, particularly at the microscale level. The system undergoes continuous performance evaluation, with statistical indicators computed daily to assess the reliability of nowcasting and forecasting outputs. A year-long pilot study in 2024 demonstrated that the assimilation of real-time observations led to significant improvements in the precision of meteorological simulations, ultimately enhancing resilience planning for IWW infrastructures.
Additionally, the deliverable examines the long-term implications of climate change on IWW infrastructure resilience by analysing ETCCDI indices under different emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results, visualized for the moderate RCP4.5 scenario, offer valuable insights into evolving climate stressors such as temperature extremes and changing precipitation patterns. These findings support strategic adaptation planning and the development of robust mitigation measures to ensure the long-term sustainability of inland waterway transport systems.